A Weight Adaptive Kalman Information Fusion Method for Attitude Measurement

An accurate attitude measurement system is crucial for a tunnel boring machine (TBM) to run along the planned axis. But the accuracy of an attitude measurement system decreases when the tunnel boring machine works under vibration of greater than 9.8 m/
$\text{s}^{{2}}$
. To solve the above problem, a fusion method of a fiber optic gyroscope and an inclinometer is investigated. In order to improve the stability of the measurement system under vibration, the weight adaptive Kalman information fusion method (WKF) is proposed. The method consists of a two-layer information fusion process. The first layer is a variance-weighted pre-processing method, which fuses the fiber optic gyroscope and inclinometer with an adaptive weight to adapt to varying vibration, and the second layer is a Kalman information fusion. Experimental results show that the WKF method can achieve a high measurement accuracy of the attitude angle under both low and strong vibration.